{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![CF](imgs/netflixCF.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Collaborative Filtering using Deep Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we will use a Deep Learning / Neural Network approach that is up and coming with recent development in machine learning and AI technologies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import keras\n",
    "from wordcloud import WordCloud\n",
    "from keras.callbacks import Callback, EarlyStopping, ModelCheckpoint\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000209, 5)\n"
     ]
    }
   ],
   "source": [
    "# Reading rating file\n",
    "dataset = pd.read_csv(\"ratings.zip\", compression='zip', sep='::',\n",
    "                 names=['real_user_id','real_movie_id','rating','timestamp'], header=None, usecols=['real_user_id','real_movie_id','rating'])\n",
    "#Convert user and movie ids from int to categories\n",
    "dataset['user_id'] = dataset['real_user_id'].astype('category').cat.codes.values\n",
    "dataset['movie_id'] = dataset['real_movie_id'].astype('category').cat.codes.values\n",
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3883, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Reading movies file\n",
    "movies = pd.read_csv('ml-1m/movies.dat', sep='::', encoding='latin-1', \n",
    "                     names=['movie_id', 'title', 'genres'], header=None)\n",
    "movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(dataset, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Building Model\n",
    "\n",
    "The idea of using deep learning is similar to that of Model-Based Matrix Factorization. In matrix factorization, we decompose our original sparse matrix into product of 2 low rank orthogonal matrices. For deep learning implementation, we don’t need them to be orthogonal, we want our model to learn the values of embedding matrix itself. The user latent features and movie latent features are looked up from the embedding matrices for specific movie-user combination. These are the input values for further linear and non-linear layers. We can pass this input to multiple relu, linear or sigmoid layers and learn the corresponding weights by any optimization algorithm (Adam, SGD, etc.).\n",
    "\n",
    "Here are the main components of my neural network:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A left embedding layer that creates a Users by Latent Factors matrix.\n",
    "- A right embedding layer that creates a Movies by Latent Factors matrix.\n",
    "- When the input to these layers are (i) a user id and (ii) a movie id, they’ll return the latent factor vectors for the user and the movie, respectively.\n",
    "- A merge layer that takes the dot product of these two latent vectors to return the predicted rating.\n",
    "\n",
    "\n",
    "I then compile the model using Mean Squared Error (MSE) as the loss function and the AdaMax learning algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def buildNN(n_users, n_movies, n_latent_factors):\n",
    "    movie_input = keras.layers.Input(shape=[1],name='Item')\n",
    "    movie_embedding = keras.layers.Embedding(n_movies + 1, n_latent_factors, name='Movie-Embedding')(movie_input)\n",
    "    movie_vec = keras.layers.Flatten(name='FlattenMovies')(movie_embedding)\n",
    "\n",
    "    user_input = keras.layers.Input(shape=[1],name='User')\n",
    "    user_embedding = keras.layers.Embedding(n_users + 1, n_latent_factors,name='User-Embedding')(user_input)\n",
    "    user_vec = keras.layers.Flatten(name='FlattenUsers')(user_embedding)\n",
    "\n",
    "    prod = keras.layers.dot([movie_vec, user_vec], axes= 1, name='DotProduct')\n",
    "    model = keras.Model([user_input, movie_input], prod)\n",
    "    # model.compile('adam', 'mean_squared_error')\n",
    "    model.compile(loss='mse', optimizer='adamax')\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "Item (InputLayer)               (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "User (InputLayer)               (None, 1)            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "Movie-Embedding (Embedding)     (None, 1, 500)       1853500     Item[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "User-Embedding (Embedding)      (None, 1, 500)       3020500     User[0][0]                       \n",
      "__________________________________________________________________________________________________\n",
      "FlattenMovies (Flatten)         (None, 500)          0           Movie-Embedding[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "FlattenUsers (Flatten)          (None, 500)          0           User-Embedding[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "DotProduct (Dot)                (None, 1)            0           FlattenMovies[0][0]              \n",
      "                                                                 FlattenUsers[0][0]               \n",
      "==================================================================================================\n",
      "Total params: 4,874,000\n",
      "Trainable params: 4,874,000\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "n_users, n_movies = len(dataset.user_id.unique()), len(dataset.movie_id.unique())\n",
    "n_latent_factors = 500\n",
    "\n",
    "model=buildNN(n_users, n_movies, n_latent_factors)\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "   ### Layers Architecture \n",
    "![NN arch](imgs/embedding-layers.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks monitor the validation loss# Callb \n",
    "# Save the model weights each time the validation loss has improved\n",
    "# callbacks = [EarlyStopping('val_loss', patience=2), \n",
    "#            ModelCheckpoint('weights.h5', save_best_only=True)]\n",
    "\n",
    "\n",
    "# history = model.fit([train.user_id, train.movie_id], train.rating, epochs=10, validation_split=0.2, verbose=2, callbacks=callbacks)\n",
    "\n",
    "# Show the best validation MAE\n",
    "# min_val_loss, idx = min((val, idx) for (idx, val) in enumerate(history.history['val_loss']))\n",
    "# print ('Minimum RMSE at epoch', '{:d}'.format(idx+1), '=', '{:.4f}'.format(abs(min_val_loss)))\n",
    "\n",
    "# pd.Series(history.history['loss']).plot(logy=True)\n",
    "# plt.xlabel(\"Epoch\")\n",
    "# plt.ylabel(\"Train Error\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('weights.h5')\n",
    "y_hat = np.round(model.predict([test.user_id, test.movie_id]), 0)\n",
    "y_true = test.rating # discrete values from 1 to 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted: 2.0 \tTrue: 2\n",
      "Predicted: 2.0 \tTrue: 2\n",
      "Predicted: 5.0 \tTrue: 5\n",
      "Predicted: 4.0 \tTrue: 4\n",
      "Predicted: 4.0 \tTrue: 5\n",
      "Predicted: 4.0 \tTrue: 4\n",
      "Predicted: 3.0 \tTrue: 3\n",
      "Predicted: 4.0 \tTrue: 4\n",
      "Predicted: 3.0 \tTrue: 3\n",
      "Predicted: 4.0 \tTrue: 4\n"
     ]
    }
   ],
   "source": [
    "#see the first 10 entries\n",
    "for i, j in zip(y_hat[:10], y_true[:10]):\n",
    "    print(\"Predicted:\", i[0], \"\\tTrue:\", j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.49612581357914837"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mean_absolute_error(y_true[0:10], y_hat[0:10])\n",
    "mean_absolute_error(y_true, y_hat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#helper functions\n",
    "def get_movie_ids(userID, rating):\n",
    "    \"\"\"\n",
    "    This method takes a user id and rating and returns\n",
    "    data frame contains only the movies ids that the \n",
    "    user has watched and rated the given rating\n",
    "    \"\"\"\n",
    "    real_user_id = dataset.loc[(dataset['user_id'] == userID)]['real_user_id'].iloc[0]\n",
    "    df = dataset.loc[(dataset['real_user_id'] == real_user_id)]\n",
    "    return list(df[dataset['rating']== rating]['real_movie_id'])\n",
    "\n",
    "def get_movies_genres(movie_ids):\n",
    "    \"\"\"\n",
    "    This method takes a list of movie ids and return their\n",
    "    genres combines in a one str\n",
    "    \"\"\"\n",
    "    genres = []\n",
    "    for movie_id in movie_ids:\n",
    "        genres.extend(list(movies[movies['movie_id']==movie_id]['genres'])[0].split('|'))\n",
    "    return ' '.join(genres)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recommend_movies(userID):\n",
    "    #----- wordcloud of user movies -----\n",
    "    user_genres = get_movies_genres(get_movie_ids(userID, rating=5))\n",
    "    wordcloud = WordCloud().generate(user_genres)\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"watched movies genres\")\n",
    "    plt.show()\n",
    "    #------------------------------------\n",
    "    \n",
    "    #preparing recommendations\n",
    "    predictedRatingsDF=pd.DataFrame(columns=['movie_id','rating'])\n",
    "    for movie_id in list(set(dataset.movie_id)):\n",
    "        predictedRating = model.predict([np.array([userID]),np.array([movie_id])])[0][0]\n",
    "        real_movie_id = dataset.loc[(dataset['movie_id'] == movie_id)]['real_movie_id'].iloc[0]\n",
    "        predictedRatingsDF.loc[predictedRatingsDF.shape[0]]=pd.Series({\"movie_id\":real_movie_id,\n",
    "                                                                       \"rating\":predictedRating})\n",
    "    df = predictedRatingsDF.sort_values(by=['rating'], ascending=False).reset_index(drop=True)[:20]\n",
    "    \n",
    "    \n",
    "    #----- wordcloud of recommended movies -----\n",
    "    text = get_movies_genres(list(df['movie_id']))\n",
    "    wordcloud = WordCloud().generate(text)\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis(\"off\")\n",
    "    plt.title(\"recommended movies genres\")\n",
    "    plt.show()\n",
    "    #-------------------------------------------\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BfaRWVxyh7gEi+48w+853yFz8kJV+Uz2eouXM8yQffzZYXRhB3xTTYuqNr9H24heI7D2E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\nCAgEAkET8/8B1sXK3UTPJPMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XzmXoqMsGAdXgpTNBtrtNNWTtAUpRZjnvHF23CkuWGV9HUFy3Ca5PcTRLaTzH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\nkpKSco5JjUBKSkrKOSY1AikpKSnnmP8PTxjoNVdelH4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2791.0</td>\n",
       "      <td>4.994636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1198.0</td>\n",
       "      <td>4.797114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>919.0</td>\n",
       "      <td>4.734626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>260.0</td>\n",
       "      <td>4.731014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1196.0</td>\n",
       "      <td>4.702335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.614853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1136.0</td>\n",
       "      <td>4.578426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1291.0</td>\n",
       "      <td>4.578344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2987.0</td>\n",
       "      <td>4.530724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3671.0</td>\n",
       "      <td>4.523731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1036.0</td>\n",
       "      <td>4.459180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>588.0</td>\n",
       "      <td>4.422738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>480.0</td>\n",
       "      <td>4.418470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1387.0</td>\n",
       "      <td>4.372151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>589.0</td>\n",
       "      <td>4.324333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1197.0</td>\n",
       "      <td>4.293082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2804.0</td>\n",
       "      <td>4.278206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1278.0</td>\n",
       "      <td>4.268912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1148.0</td>\n",
       "      <td>4.236254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2716.0</td>\n",
       "      <td>4.236150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id    rating\n",
       "0     2791.0  4.994636\n",
       "1     1198.0  4.797114\n",
       "2      919.0  4.734626\n",
       "3      260.0  4.731014\n",
       "4     1196.0  4.702335\n",
       "5        1.0  4.614853\n",
       "6     1136.0  4.578426\n",
       "7     1291.0  4.578344\n",
       "8     2987.0  4.530724\n",
       "9     3671.0  4.523731\n",
       "10    1036.0  4.459180\n",
       "11     588.0  4.422738\n",
       "12     480.0  4.418470\n",
       "13    1387.0  4.372151\n",
       "14     589.0  4.324333\n",
       "15    1197.0  4.293082\n",
       "16    2804.0  4.278206\n",
       "17    1278.0  4.268912\n",
       "18    1148.0  4.236254\n",
       "19    2716.0  4.236150"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommend_movies(userID = 2222)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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